where \(\phi _l\)  denotes the output of \(l-\) th layer from the pretarined VGG-19 model.
We also adopt contextual loss proposed in [1]}, which is designed for image generation that naturally handles tasks with non-aligned training data and is very suitable for our task. The contexttual loss is defined as follow.
\(\mathcal {L}_{con}=-\log (\mathop {CX}(\phi _l(x),\phi _l(y))\) 
